循环数据帧时添加数据帧名称
我编写了以下代码来在不同的数据帧（名为“Drought”、“Flashflood”等）上循环相同的函数。我很高兴看到它有效，但我正在尝试确定如何获取数据帧的名称以附加训练和测试分数。有人可以指导我这里缺少什么吗？如果我像现在一样这样做，所有名称都会发布在底部的每一行中，但我只想要相应的名称。同样，我得到的输出将每个新数组附加在一起，但我的理解是附加只会将新项目添加到列表中？
例如，我得到的结果是：
[(0.11995478823013683, -0.07264567664161303), (0.11998113643282327, -0.034458152253100005)]
但我期望这样：
[("Drought",0.11995478823013683, -0.07264567664161303)]
[("Flashflood",0.11998113643282327, -0.034458152253100005)]
这是代码：
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
df_list = [Drought, Flashflood, Flood, Gale]
names = ['Drought','Flashflood','Flood','Gale']
knn_r_acc = []
rmse_val = [] #to store rmse values for different dataframes
for df in df_list:
X = df[['Year.Month','IDH.M_2000','Population','IDH.M_2010']]
y = df['Deceased'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
#Scaling
scaler = MinMaxScaler(feature_range=(0, 1))
x_train_scaled = scaler.fit_transform(X_train)
x_train = pd.DataFrame(x_train_scaled)
x_test_scaled = scaler.fit_transform(X_test)
x_test = pd.DataFrame(x_test_scaled)
model = neighbors.KNeighborsRegressor(n_neighbors = 3, weights = 'uniform')
model.fit(x_train, y_train) #fit the model
pred=model.predict(x_test) #make prediction on test set
error = sqrt(mean_squared_error(y_test,pred)) #calculate rmse
rmse_val.append(error) #store rmse values
#print('Model= ' , df, 'is:', error)
knn.fit(X_train,y_train)
test_score = knn.score(X_test,y_test)
train_score = knn.score(X_train,y_train)
#print(test_score)
#print(train_score)
knn_r_acc.append((names,train_score, test_score))
print(knn_r_acc)
I wrote the following code to loop the same function over different dataframes (named "Drought", "Flashflood",etc). I was happy to see that it worked, but I'm trying to determine how to get the names of the dataframes to append with the train and test scores. Can someone please guide me on what I'm missing here? If I do it like I currently have it, all the names post in each row at the bottom, but I only want the corresponding one. Similarly, the output I get appends each new array together, but my understanding was that append would just add a new item to a list?
For example I'm getting this as a result:
[(0.11995478823013683, -0.07264567664161303), (0.11998113643282327, -0.034458152253100005)]
But I would expect this:
[("Drought",0.11995478823013683, -0.07264567664161303)]
[("Flashflood",0.11998113643282327, -0.034458152253100005)]
Here's the code:
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
df_list = [Drought, Flashflood, Flood, Gale]
names = ['Drought','Flashflood','Flood','Gale']
knn_r_acc = []
rmse_val = [] #to store rmse values for different dataframes
for df in df_list:
X = df[['Year.Month','IDH.M_2000','Population','IDH.M_2010']]
y = df['Deceased'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
#Scaling
scaler = MinMaxScaler(feature_range=(0, 1))
x_train_scaled = scaler.fit_transform(X_train)
x_train = pd.DataFrame(x_train_scaled)
x_test_scaled = scaler.fit_transform(X_test)
x_test = pd.DataFrame(x_test_scaled)
model = neighbors.KNeighborsRegressor(n_neighbors = 3, weights = 'uniform')
model.fit(x_train, y_train) #fit the model
pred=model.predict(x_test) #make prediction on test set
error = sqrt(mean_squared_error(y_test,pred)) #calculate rmse
rmse_val.append(error) #store rmse values
#print('Model= ' , df, 'is:', error)
knn.fit(X_train,y_train)
test_score = knn.score(X_test,y_test)
train_score = knn.score(X_train,y_train)
#print(test_score)
#print(train_score)
knn_r_acc.append((names,train_score, test_score))
print(knn_r_acc)
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在你的例子中，
names实际上是一个完整的列表/数组。您可以使用索引变量来实现它。因此，在循环开始之前，添加：
并在循环内添加，如下所示：
In your case,
namesis actually a whole list / array.You can implement it using an index variable. So before the loop starts, add:
and inside the loop, append like so: